[HTML][HTML] Towards the adoption of quantitative computed tomography in the management of interstitial lung disease
The shortcomings of qualitative visual assessment have led to the development of computer-
based tools to characterise and quantify disease on high-resolution computed tomography …
based tools to characterise and quantify disease on high-resolution computed tomography …
Using deep learning to annotate the protein universe
Understanding the relationship between amino acid sequence and protein function is a long-
standing challenge with far-reaching scientific and translational implications. State-of-the-art …
standing challenge with far-reaching scientific and translational implications. State-of-the-art …
Decoding biology with massively parallel reporter assays and machine learning
Massively parallel reporter assays (MPRAs) are powerful tools for quantifying the impacts of
sequence variation on gene expression. Reading out molecular phenotypes with …
sequence variation on gene expression. Reading out molecular phenotypes with …
Interpreting neural networks for biological sequences by learning stochastic masks
Sequence-based neural networks can learn to make accurate predictions from large
biological datasets, but model interpretation remains challenging. Many existing feature …
biological datasets, but model interpretation remains challenging. Many existing feature …
An improved deep learning model for hierarchical classification of protein families
PD Sandaruwan, CT Wannige - Plos one, 2021 - journals.plos.org
Although genes carry information, proteins are the main role player in providing all the
functionalities of a living organism. Massive amounts of different proteins involve in every …
functionalities of a living organism. Massive amounts of different proteins involve in every …
Tuned Fitness Landscapes for Benchmarking Model-Guided Protein Design
Advancements in DNA synthesis and sequencing technologies have enabled a novel
paradigm of protein design where machine learning (ML) models trained on experimental …
paradigm of protein design where machine learning (ML) models trained on experimental …
Improving protein domain classification for third-generation sequencing reads using deep learning
Background With the development of third-generation sequencing (TGS) technologies,
people are able to obtain DNA sequences with lengths from 10s to 100s of kb. These long …
people are able to obtain DNA sequences with lengths from 10s to 100s of kb. These long …
Machine learning model interpretations explain T cell receptor binding
B Carter, J Krog, ME Birnbaum, DK Gifford - bioRxiv, 2023 - biorxiv.org
T cells mediate immune responses against pathogens and cancer through T cell receptors
(TCRs) that recognize foreign peptides presented on the cell surface by Major …
(TCRs) that recognize foreign peptides presented on the cell surface by Major …
A Model for Optimization of Feature Extraction in Recognition Systems Based on Parallel Algorithms
AR Aliev, EA Ismayilov - 2023 5th International Conference on …, 2023 - ieeexplore.ieee.org
Research in improving image recognition systems is essential due to the unreliability of
existing systems. The key idea in this paper is to use human-based features for classifying …
existing systems. The key idea in this paper is to use human-based features for classifying …
[图书][B] Predicting, Engineering and Interpreting Gene Regulatory Sequences and Proteins with Deep Learning
JSA Linder - 2021 - search.proquest.com
The vast majority of the 3.1 billion base-pairs in the (haploid) human genome do not code for
a particular protein, yet mutations in these non-coding regions can have a profound impact …
a particular protein, yet mutations in these non-coding regions can have a profound impact …